Perturbation-Regularized Open-Vocabulary Remote Sensing Segmentation with Unified Multi-Domain Evaluation
Department of Electronic Engineering and Information Science, University of Science and Technology of China（电子工程与信息科学系，中国科学技术大学）；School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（人工智能学院，光学与电子学系（iOPEN），西北工业大学）；Institute of Artificial Intelligence (TeleAI), China Telecom, P. R. China（人工智能研究院（TeleAI），中国电信，中华人民共和国）
Bingyu Li, Tao Huo, Haocheng Dong, Da Zhang, Zhiyuan Zhao, Junyu Gao, Xuelong Li
Open-vocabulary remote sensing image segmentation (OVRSIS) aims to segment text-specified categories beyond a fixed label space. Its key challenge is to maintain reliable pixel--text correspondence under substantial appearance variation in remote-sensing imagery. Existing methods often rely on specialized vision--language representations or complex adaptation mechanisms, yet still perform deterministic matching between fixed text prototypes and individual visual features. We instead formulate OVRSIS as a \emph{distributional pixel--text alignment} problem and propose \textbf{Pi-Seg}, a lightweight perturbation-based framework. Pi-Seg introduces learnable variations into textual prototypes and dense visual features before alignment. Segmentation supervision encourages perturbations that enlarge the target-to-distractor margin while suppressing harmful feature shifts. Pi-Seg consistently improves performance under the existing \textit{single-source} OVRSISBench protocols. To evaluate OVRSIS beyond source-specific training settings, we further introduce a unified \textit{multi-source} protocol and construct \textbf{GlobalRSOV95K}, containing approximately 95K densely annotated images and 35 semantic categories. Models are trained on this shared dataset and evaluated on an image-disjoint suite of 10 downstream datasets. The complete benchmark covers more than 170K images and 122 categories. Experiments under both single-source and multi-source protocols show that Pi-Seg improves cross-dataset transfer and generalizes effectively to practical geospatial segmentation tasks, while GlobalRSOV95K provides a common foundation for transferable OVRSIS research. \footnote{\url{https://github.com/LiBingyu01/Pi-Seg}}
School of Electronic Information, Wuhan University of Science and Technology（武汉科技大学电子信息学院）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；Oriental Space Port Research Institute（东方航天港研究院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）
SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation
Nanyang Technological University（南洋理工大学）；MoE Key Laboratory of Interdisciplinary Research of Computation and Economics, Shanghai University of Finance and Economics（上海财经大学经济与计算交叉研究重点实验室）；Institute for Infocomm Research (I 2 R), A*STAR, Singapore（新加坡资讯与通讯研究院）
SegEarth-OV3: Exploring SAM 3 for Open-Vocabulary Semantic Segmentation in Remote Sensing Images
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；School of Information and Communications Engineering, Xi’an Jiaotong University（西安交通大学信息与通信工程学院）；Ministry of Education Key Lab of Intelligent Network Security, Xi’an, China（教育部智能网络安全重点实验室）；Pazhou Laboratory (Huangpu), Radarweg 29, Guangzhou, China（琶洲实验室（黄埔），雷达路29号，广州，中国）
A Geometric-Transformation Feature-Adaptive Manifold Restoration Method for Open-Vocabulary Semantic Segmentation of Remote Sensing Images
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology（南京信息工程大学遥感与测绘工程学院）；Nanjing Center, China Geological Survey（中国地质调查局南京中心）；School of Automation, Southeast University（东南大学自动化学院）
Prompt-Calibrated SAM 3 for Open-Vocabulary Remote Sensing Semantic Segmentation
School of Information Science and Technology, Yunnan Normal University（云南师范大学信息科学与技术学院）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；College of Computer Science, Beijing University of Technology（北京工业大学计算机学院）
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）
Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images
School of Software Engineering, Xi’an Jiaotong University（软件工程学院，西安交通大学）；School of Computer Science and Technology and Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi’an Jiaotong University（计算机科学与技术学院和教育部智能网络与网络安全重点实验室，西安交通大学）；School of Automation, Xi’an Jiaotong University（自动化学院，西安交通大学）；College of Artificial Intelligence, Xi’an Jiaotong University（人工智能学院，西安交通大学）；School of Mathematics and Statistics and Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi’an Jiaotong University（数学与统计学院和教育部智能网络与网络安全重点实验室，西安交通大学）
AdaptOVCD: Training-Free Open-Vocabulary Remote Sensing Change Detection via Adaptive Information Fusion
Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences (CAS)（光谱成像技术重点实验室、光学与精密机械研究所、中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Artificial Intelligence (TeleAI), China Telecom（人工智能研究所（TeleAI），中国电信）；College of Future Information Technology, Fudan University（复旦大学未来信息技术学院）；State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS（资源与环境信息系统国家重点实验室、地理科学与自然资源研究所、中国科学院）；Key Laboratory of Low Altitude Geographic Information and Air Route, CAAC（低空地理信息与航线重点实验室、民航局）；School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（人工智能、光学与电子学院（iOPEN），西北工业大学）
Taming SAM3 in the Wild: A Concept Bank for Open-Vocabulary Segmentation
Department of Electrical and Computer Engineering, Sungkyunkwan University（Sungkyunkwan 大学电气与计算机工程系）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（中国电子科技大学计算机科学与工程学院）
ReA-OVCD: Training-Free Open-Vocabulary Change Detection via Semantic-Spatial Reliability Assessment
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；College of Surveying and Geo-Informatics, Tongji University（同济大学测绘与地理信息学院）
In recent years, an ever-increasing number of remote satellites are orbiting the Earth which streams vast amount of visual data to support a wide range of civil, public and military applications. One of the key information obtained from satellite imagery is to produce and update spatial maps of built environment due to its wide coverage with high resolution data. However, reconstructing spatial maps from satellite imagery is not a trivial vision task as it requires reconstructing a scene or object with high-level representation such as primitives. For the last decade, significant advancement i…
Training-Free Open-Vocabulary Visual Grounding for Remote Sensing Images and Videos
School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Interdisciplinary Institute of Artificial Intelligence, Xidian University（西安电子科技大学跨学科人工智能研究院）；School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；Northwest Institute of Nuclear Technology（西北核技术研究所）；School of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）
GeoSeg-OV: Bridging Geospatial Gaps with Structural Guidance for Open-Vocabulary Remote Sensing Segmentation
School of Computer Science, China University of Geosciences（中国地质大学计算机学院）；Systems Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）系统中心）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机系）；School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院）；Data Science and AI Innovation Research Promotion Center, Shiga University（滋贺大学数据科学与人工智能创新研究促进中心）
U.S. Army Corps of Engineers, Cold Regions Research and Engineering Laboratory（美国陆军工程兵团寒区研究与工程实验室）
As Arctic conditions rapidly change, human activity in the Arctic will continue to increase and so will the need for high-resolution observations of sea ice. While satellite imagery can provide high spatial resolution, it is temporally sparse and significant ice deformation can occur between observations. This makes it difficult to apply feature tracking or image correlation techniques that require persistent features to exist between images. With this in mind, we propose a technique based on optimal transport, which is commonly used to measure differences between probability distributions. Wh…
In remote sensing images, the presence of thick cloud accompanying cloud shadow is a high probability event, which can affect the quality of subsequent processing and limit the scenarios of application. Hence, removing the thick cloud and cloud shadow as well as recovering the cloud-contaminated pixels is indispensable to make good use of remote sensing images. In this paper, a novel thick cloud removal method for remote sensing images based on temporal smoothness and sparsity-regularized tensor optimization (TSSTO) is proposed. The basic idea of TSSTO is that the thick cloud and cloud shadow …
OS-W2S: An Automatic Labeling Engine for Language-Guided Open-Set Aerial Object Detection
Nanjing University of Science and Technology（南京理工大学）；Intellifusion Inc.（Intellifusion公司）；Northwestern Polytechnical University（西北工业大学）；Zhejiang Lab（浙江实验室）；Yan’an University（延安大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object Recognition
Aerospace Information Research Institute, Chinese Academy of Sciences（航天信息研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Harbin Institute of Technology（哈尔滨工业大学）；Shanghai Jiao Tong University（上海交通大学）；University of Wollongong（沃林戈大学）；SAIS, Shanghai Jiao Tong University（上海交通大学国际关系学院）
Latent Space Guided Scenario Sampling for Multimodal Segmentation Under Missing Modalities
organization= Department of Computer Engineering, Ankara University, Ankara, T \"u rkiye；organization= Department of Modeling；Simulation, Graduate School of Informatics, METU, Ankara, T \"u rkiye
RSAJEV: Training-Free Abstaining Prototype Memory for Eliminating Silent Hallucination in JEV-Based Remote Sensing Classification
School of Computer Science and Engineering, Anhui University of Science and Technology（安徽理工大学计算机科学与工程学院）；School of Electrical Automation and Information Engineering, Tianjin University（天津大学电气自动化与信息工程学院）；State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining（无人采矿数字智能技术国家重点实验室）；Guangxi Key Laboratory of Multi-Source Information Mining and Security, Guangxi Normal University（广西师范大学广西多源信息挖掘与安全重点实验室）
